OpenRaiser/Intern-Atlas huggingface dataset:
QoS Constrained Power Minimization in the MISO Broadcast Channel with Imperfect CSI
Venue: arXiv cs.IT 2015
Year: 2015

We consider the design of linear precoders and receivers in a Multiple-Input Single-Output (MISO) Broadcast Channel (BC). We aim at minimizing the transmit power while fullfiling a set of per-user Quality-of-Service (QoS) constraints expressed in terms of per-user average rate requirements. The Channel State Information (CSI) is assumed to be perfectly known at the receivers but only partially at the transmitter. To solve the problem we transform the QoS constraints into Minimum Mean Square Error (MMSE) constraints. We then leverage the MSE duality between the BC and the Multiple Access Channel (MAC), as well as standard interference functions in the dual MAC, to perform power minimization by means of an Alternating Optimization (AO) algorithm. Problem feasibility is also studied to determine whether the QoS constraints can be fulfilled or not. Finally, we present an algorithm to balance the average rates and manage situations that may be unfeasible or lead to an unacceptably high transmit power.
ShapeNet: An Information-Rich 3D Model Repository
Venue: arXiv cs.GR 2015
Year: 2015

We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.
Ambient RF Energy Harvesting in Ultra-Dense Small Cell Networks: Performance and Trade-offs
Venue: arXiv cs.NI 2015
Year: 2015

In order to minimize electric grid power consumption, energy harvesting from ambient RF sources is considered as a promising technique for wireless charging of low-power devices. To illustrate the design considerations of RF-based ambient energy harvesting networks, this article first points out the primary challenges of implementing and operating such networks, including non-deterministic energy arrival patterns, energy harvesting mode selection, energy-aware cooperation among base stations (BSs), etc. A brief overview of the recent advancements and a summary of their shortcomings are then provided to highlight existing research gaps and possible future research directions. To this end, we investigate the feasibility of implementing RF-based ambient energy harvesting in ultra-dense small cell networks (SCNs) and examine the related trade-offs in terms of the energy efficiency and signal-to-interference-plus-noise ratio (SINR) outage probability of a typical user in the downlink. Numerical results demonstrate the significance of deploying a mixture of on-grid small base stations (SBSs)~(powered by electric grid) and off-grid SBSs~(powered by energy harvesting) and optimizing their corresponding proportions as a function of the intensity of active SBSs in the network.
Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive MIMO systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this letter, we propose a joint CSIT acquisition scheme to reduce the overhead. Particularly, unlike conventional schemes where each user individually estimates its own channel and then feed it back to the base station (BS), we propose that all scheduled users directly feed back the pilot observation to the BS, and then joint CSIT recovery can be realized at the BS. We further formulate the joint CSIT recovery problem as a low-rank matrix completion problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the correlation among users. Finally, we propose a hybrid low-rank matrix completion algorithm based on the singular value projection to solve this problem. Simulations demonstrate that the proposed scheme can provide accurate CSIT with lower overhead than conventional schemes.
New Deterministic Algorithms for Solving Parity Games
Venue: arXiv cs.CC 2015
Year: 2015

We study parity games in which one of the two players controls only a small number $k$ of nodes and the other player controls the $n-k$ other nodes of the game. Our main result is a fixed-parameter algorithm that solves bipartite parity games in time $k^{O(\sqrt{k})}\cdot O(n^3)$, and general parity games in time $(p+k)^{O(\sqrt{k})} \cdot O(pnm)$, where $p$ is the number of distinct priorities and $m$ is the number of edges. For all games with $k = o(n)$ this improves the previously fastest algorithm by Jurdzi{ń}ski, Paterson, and Zwick (SICOMP 2008). We also obtain novel kernelization results and an improved deterministic algorithm for graphs with small average degree.
Adaptive Neural Control for Mobile Robots Autonomous Navigation
Venue: arXiv cs.RO 2015
Year: 2015

This paper presents a combined strategy for tracking a non-holonomic mobile robot which works under certain operating conditions for system parameters and disturbances. The strategy includes kinematic steering and velocity dynamics learning of mobile robot system simultaneously. In the learning controller (neural network based controller) the velocity dynamics learning control takes part in tracking of the reference velocity trajectory by learning the inverse function of robot dynamics while the reference velocity control input plays a role in stabilizing the kinematic steering system to the desired reference model of kinematic system even without using the assumption of perfect velocity tracking.
Evaluation of Object Detection Proposals Under Condition Variations
Venue: arXiv cs.CV 2015
Year: 2015

Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines the Selective Search and EdgeBoxes. We tested these three methods under environmental variations. Our experiments demonstrate the outperformance of the combination method under illumination and view point variations.
An Energy Consumption Model for IEEE 802.11ah WLANs
Venue: arXiv cs.NI 2015
Year: 2015

One of the main challenges when designing a new self-powered wireless sensor network (WSN) technology is the vast operational dependence on its scarce energy resources. Therefore, a thorough identification and characterisation of the main energy consumption processes may lay the foundation for developing further mechanisms aimed to make a more efficient use of devices' batteries. This paper provides an energy consumption model for IEEE 802.11ah WLANs operating in power saving mode, which are expected to become one of the technology drivers in the development of the Internet of Things (IoT) in the next years. Given the network characteristics, the presented analytical model is able to provide an estimation of the average energy consumed by a station as well as to predict its battery lifetime. Once the model has been validated, we use it to obtain the optimal IEEE 802.11ah power saving parameters in several IoT key scenarios, validating that the parameters provided by the IEEE 802.11ah Task Group are already a very good choice.
Hierarchical Quantification of Synergy in Channels
Venue: arXiv cs.IT 2015
Year: 2015

The decomposition of channel information into synergies of different order is an open, active problem in the theory of complex systems. Most approaches to the problem are based on information theory, and propose decompositions of mutual information between inputs and outputs in se\-veral ways, none of which is generally accepted yet. We propose a new point of view on the topic. We model a multi-input channel as a Markov kernel. We can project the channel onto a series of exponential families which form a hierarchical structure. This is carried out with tools from information geometry, in a way analogous to the projections of probability distributions introduced by Amari. A Pythagorean relation leads naturally to a decomposition of the mutual information between inputs and outputs into terms which represent single node information, pairwise interactions, and in general n-node interactions. The synergy measures introduced in this paper can be easily evaluated by an iterative scaling algorithm, which is a standard procedure in information geometry.
Robust Dictionary based Data Representation
Venue: arXiv cs.CV 2015
Year: 2015

The robustness to noise and outliers is an important issue in linear representation in real applications. We focus on the problem that samples are grossly corrupted, which is also the 'sample specific' corruptions problem. A reasonable assumption is that corrupted samples cannot be represented by the dictionary while clean samples can be well represented. This assumption is enforced in this paper by investigating the coefficients of corrupted samples. Concretely, we require the coefficients of corrupted samples be zero. In this way, the representation quality of clean data can be assured without the effect of corrupted data. At last, a robust dictionary based data representation approach and its sparse representation version are proposed, which have directive significance for future applications.
Understanding Customers' Evaluations Through Mining Airline Reviews
Venue: arXiv cs.SI 2015
Year: 2015

Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn customer expectations and requirements. Airline customers have different characteristics and if passenger reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction by improving provided services. In this study, we investigate customer review data for in-flight services of airline companies and draw customer models with respect to such data. In this sense, we apply two approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are grouped into categories based on features such as cabin flown types, experienced airline companies. In clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We apply multivariate regression analysis to model customer groups in both cases. During this, we try to understand how customers evaluate the given services and what dominant characteristics of in-flight services can be from the customer viewpoint.
The Entropy Gain of Linear Time-Invariant Filters and Some of its Implications
Venue: arXiv cs.IT 2015
Year: 2015

We study the increase in per-sample differential entropy rate of random sequences and processes after being passed through a non minimum-phase (NMP) discrete-time, linear time-invariant (LTI) filter G. For such filters and random processes, it has long been established that this entropy gain, Gain(G), equals the integral of log|G(exp(jw))|. It is also known that, if the first sample of the impulse response of G has unit-magnitude, then this integral equals the sum of the logarithm of the magnitudes of the non-minimum phase zeros of G, say B(G). In this note, we begin by showing that existing time-domain proofs of these results, which consider finite length-n sequences and then let n tend to infinity, have neglected significant mathematical terms and, therefore, are inaccurate. We discuss some of the implications of this oversight when considering random processes. We then present a rigorous time-domain analysis of the entropy gain of LTI filters for random processes. In particular, we show that the entropy gain between equal-length input and output sequences is upper bounded by B(G) and arises if and only if there exists an output additive disturbance with finite differential entropy (no matter how small) or a random initial state. Instead, when comparing the input differential entropy to that of the entire (longer) output of G, the entropy gain equals B(G) without the need for additional exogenous random signals. We illustrate some of the consequences of these results by presenting their implications in three different problems. Specifically: a simple derivation of the rate-distortion function for Gaussian non-stationary sources, conditions for equality in an information inequality of importance in networked control problems, and an observation on the capacity of auto-regressive Gaussian channels with feedback.
A Design of Endurance Queue for Co-Existing Systems in Multi-Programmed Environments
Venue: arXiv cs.PF 2015
Year: 2015

These days enterprise applications try to integrate online processing and batch jobs into a common software stack for seamless monitoring and driverless operations. Continuous integration of these systems results in choking of the poorly performing sub-systems, when the service demand and throughput are not synchronized. A poorly performing sub-system may become a serious performance bottleneck for the entire system if its serviceability and the capacity is over utilized by increased service demand from upstream systems. From all the integrated sub-systems, queuing systems are majorly categorized as choking elements due to their limited service length and lack of processing details. The situation becomes more pronounced in multiprogramming environments where the queue performance exponentially degrades with increased degree of multiprogramming at upstream levels. This paper presents an approach to compute the queue length and devise a distribution model such that the queue length is dynamically adjusted depending on the sudden growth or decline of transmission packets. The idea is to design a heat map of the memory and correlate it with the queue length distribution. With each degree of multi-programmability, the data processing logic is adjusted by the distribution model to arrive at an endurance level queue for long term service under variable load conditions. It will take away the current implementation of using delayed processing logic and/or batch processing of data at downstream systems.
Finding HeavyPaths in Weighted Graphs and a Case-Study on Community Detection
Venue: arXiv cs.DS 2015
Year: 2015

A heavy path in a weighted graph represents a notion of connectivity and ordering that goes beyond two nodes. The heaviest path of length l in the graph, simply means a sequence of nodes with edges between them, such that the sum of edge weights is maximum among all paths of length l. It is trivial to state the heaviest edge in the graph is the heaviest path of length 1, that represents a heavy connection between (any) two existing nodes. This can be generalized in many different ways for more than two nodes, one of which is finding the heavy weight paths in the graph. In an influence network, this represents a highway for spreading information from a node to one of its indirect neighbors at distance l. Moreover, a heavy path implies an ordering of nodes. For instance, we can discover which ordering of songs (tourist spots) on a playlist (travel itinerary) is more pleasant to a user or a group of users who enjoy all songs (tourist spots) on the playlist (itinerary). This can also serve as a hard optimization problem, maximizing different types of quantities of a path such as score, flow, probability or surprise, defined as edge weight. Therefore, if one can solve the Heavy Path Problem (HPP) efficiently, they can as well use HPP for modeling and reduce other complex problems to it.
An Uncertainty-Aware Approach for Exploratory Microblog Retrieval
Venue: arXiv cs.IR 2015
Year: 2015

Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result: the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data.
The Transitional Behavior of Interference in Millimeter Wave Networks and Its Impact on Medium Access Control
Venue: arXiv cs.IT 2015
Year: 2015

Millimeter wave (mmWave) communication systems use large number of antenna elements that can potentially overcome severe channel attenuation by narrow beamforming. Narrow-beam operation in mmWave networks also reduces multiuser interference, introducing the concept of noise-limited wireless networks as opposed to interference-limited ones. The noise-limited or interference-limited regime heavily reflects on the medium access control (MAC) layer throughput and on proper resource allocation and interference management strategies. Yet, these regimes are ignored in current approaches to mmWave MAC layer design, with the potential disastrous consequences on the communication performance. In this paper, we investigate these regimes in terms of collision probability and throughput. We derive tractable closed-form expressions for the collision probability and MAC layer throughput of mmWave ad hoc networks, operating under slotted ALOHA. The new analysis reveals that mmWave networks may exhibit a non-negligible transitional behavior from a noise-limited regime to an interference-limited one, depending on the density of the transmitters, density and size of obstacles, transmission probability, operating beamwidth, and transmission power. Such transitional behavior necessitates a new framework of adaptive hybrid resource allocation procedure, containing both contention-based and contention-free phases with on-demand realization of the contention-free phase. Moreover, the conventional collision avoidance procedure in the contention-based phase should be revisited, due to the transitional behavior of interference, to maximize throughput/delay performance of mmWave networks. We conclude that, unless proper hybrid schemes are investigated, the severity of the transitional behavior may significantly reduce throughput/delay performance of mmWave networks.
A machine learning approach to anomaly-based detection on Android platforms
Venue: arXiv cs.CR 2015
Year: 2015

The emergence of mobile platforms with increased storage and computing capabilities and the pervasive use of these platforms for sensitive applications such as online banking, e-commerce and the storage of sensitive information on these mobile devices have led to increasing danger associated with malware targeted at these devices. Detecting such malware presents inimitable challenges as signature-based detection techniques available today are becoming inefficient in detecting new and unknown malware. In this research, a machine learning approach for the detection of malware on Android platforms is presented. The detection system monitors and extracts features from the applications while in execution and uses them to perform in-device detection using a trained K-Nearest Neighbour classifier. Results shows high performance in the detection rate of the classifier with accuracy of 93.75%, low error rate of 6.25% and low false positive rate with ability of detecting real Android malware.
Learning Deep Features for Discriminative Localization
Venue: arXiv cs.CV 2015
Year: 2015

In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that can be applied to a variety of tasks. Despite the apparent simplicity of global average pooling, we are able to achieve 37.1% top-5 error for object localization on ILSVRC 2014, which is remarkably close to the 34.2% top-5 error achieved by a fully supervised CNN approach. We demonstrate that our network is able to localize the discriminative image regions on a variety of tasks despite not being trained for them
Embedding approximately low-dimensional $\ell_2^2$ metrics into $\ell_1$
Venue: arXiv cs.DS 2015
Year: 2015

Goemans showed that any $n$ points $x_1, \dotsc x_n$ in $d$-dimensions satisfying $\ell_2^2$ triangle inequalities can be embedded into $\ell_{1}$, with worst-case distortion at most $\sqrt{d}$. We extend this to the case when the points are approximately low-dimensional, albeit with average distortion guarantees. More precisely, we give an $\ell_{2}^{2}$-to-$\ell_{1}$ embedding with average distortion at most the stable rank, $\mathrm{sr}(M)$, of the matrix $M$ consisting of columns $\{x_i-x_j\}_{i<j}$. Average distortion embedding suffices for applications such as the Sparsest Cut problem. Our embedding gives an approximation algorithm for the \sparsestcut problem on low threshold-rank graphs, where earlier work was inspired by Lasserre SDP hierarchy, and improves on a previous result of the first and third author [Deshpande and Venkat, In Proc. 17th APPROX, 2014]. Our ideas give a new perspective on $\ell_{2}^{2}$ metric, an alternate proof of Goemans' theorem, and a simpler proof for average distortion $\sqrt{d}$. Furthermore, while the seminal result of Arora, Rao and Vazirani giving a $O(\sqrt{\log n})$ guarantee for Uniform Sparsest Cut can be seen to imply Goemans' theorem with average distortion, our work opens up the possibility of proving such a result directly via a Goemans'-like theorem.
On the Relation between two Rotation Metrics
Venue: arXiv cs.CV 2015
Year: 2015

In their work "Global Optimization through Rotation Space Search", Richard Hartley and Fredrik Kahl introduce a global optimization strategy for problems in geometric computer vision, based on rotation space search using a branch-and-bound algorithm. In its core, Lemma 2 of their publication is the important foundation for a class of global optimization algorithms, which is adopted over a wide range of problems in subsequent publications. This lemma relates a metric on rotations represented by rotation matrices with a metric on rotations in axis-angle representation. This work focuses on a proof for this relationship, which is based on Rodrigues' Rotation Theorem for the composition of rotations in axis-angle representation.